Lidar-IMU Calibration with Point-Cloud Shape Features
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Solution Overview
Problem
Conventional Simultaneous Localization and Mapping (SLAM) systems for automatic driving face challenges due to complex operations, leading to high computational loads and prolonged processing times for localization and mapping, and sensors like GPS, IMU, and lidar are not synchronized, affecting the accuracy of high-definition road map generation.
Innovation Solution
A calibration method for lidar and IMU using features from specific objects in point cloud data, involving placing point cloud data on a predefined world coordinate system, extracting regions for calibration, identifying objects like cylindrical shapes and ground, and performing calibration through loss functions and particle swarm optimization to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SLAM systems are used for localization and mapping, then comprehensive 3D map generation is achieved, but computational load increases and processing time is prolonged
Solution Approach 1:
The patent extracts and removes unnecessary complex operations from the conventional SLAM system. By identifying and eliminating redundant computational steps in the localization and mapping process, the system achieves faster processing speeds while maintaining essential functionality for generating accurate 3D maps.
Solution Approach 2:
The patent segments the complex SLAM operation into distinct modular components. By dividing the localization and mapping process into separate functional modules, the system can process different aspects independently and in parallel, reducing overall computational load and processing time while maintaining comprehensive map generation capabilities.
2Loss of information
If multiple sensors (GPS, IMU, lidar) are used for data collection, then information completeness is improved, but synchronization difficulties arise affecting accuracy
Solution Approach 1:
The patent merges the data from multiple sensors (GPS, IMU, lidar) into a unified processing framework. By combining sensor inputs and processing them together rather than separately, the system eliminates synchronization issues between different sensors while maintaining complete information from all sources, achieving both information completeness and temporal alignment.
3Adaptability or versatility
If sensors are installed at different physical positions, then system flexibility is improved, but calibration complexity increases
Solution Approach 1:
The patent implements a self-calibration mechanism that automatically determines the spatial relationships between sensors installed at different positions. The system uses environmental features and sensor data correlations to compute relative positions and orientations without requiring manual calibration procedures, thereby maintaining installation flexibility while eliminating calibration complexity.
Data Source
AI summary
Proposed is a calibration method for a lidar and an IMU using features according to the shape of a specific object included in point cloud data. The method may include placing point cloud data acquired by a lidar mounted on a vehicle on a predefined world coordinate system, by a data generator, extracting a region to be used for calibration from the placed point cloud data, by the data generator, identifying at least one object included in the extracted region, by the data generator, and performing calibration on the point cloud data by fitting point cloud included in the at least one identified object to a pre-stored model, by the data generator. The present method is a technology developed with support from the Ministry of Land/KAIA (Project No. RS-2021-KA160637).